The Geography of Inequality and the Redistribution Gap

The distribution of post-tax inequality is sharply divided by geography, with the highest levels concentrated in Latin America

TidyTuesday
Data Visualization
R Programming
2025
An analysis of income inequality before and after taxes across countries and regions using TidyTuesday data. This visualization reveals how geographic location and policy choices determine inequality
Author

Steven Ponce

Published

August 4, 2025

Figure 1: Two-panel chart showing income inequality patterns. The left panel displays density curves of post-tax inequality by world region, with Latin America (highlighted in dark purple) exhibiting the highest levels of inequality. Right panel shows a horizontal bar chart of 15 countries with minimal redistribution effects, ordered from highest to lowest. The Dominican Republic (0.52 pre-tax Gini, 0.008 redistribution) and Brazil (0.59 pre-tax Gini, 0.079 redistribution) are highlighted in dark colors, demonstrating that Latin American countries have high market inequality but minimal government redistribution, compared to countries like Norway (0.39 pre-tax Gini, 0.152 redistribution)

Steps to Create this Graphic

1. Load Packages & Setup

Show code
```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
        tidyverse,  # Easily Install and Load the 'Tidyverse'
    ggtext,     # Improved Text Rendering Support for 'ggplot2'
    showtext,   # Using Fonts More Easily in R Graphs
    janitor,    # Simple Tools for Examining and Cleaning Dirty Data
    scales,     # Scale Functions for Visualization
    glue,       # Interpreted String Literals
    ggridges,   # Ridgeline Plots in 'ggplot2'
    patchwork   # The Composer of Plots
  )})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 12,
  height = 12,
  units  = "in",
  dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

Show code
```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false

tt <- tidytuesdayR::tt_load(2025, week = 31)

income_inequality_processed <- tt$income_inequality_processed |> clean_names()
income_inequality_raw <- tt$income_inequality_raw |> clean_names()

tidytuesdayR::readme(tt)
rm(tt)
```

3. Examine the Data

Show code
```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(income_inequality_processed)
glimpse(income_inequality_raw)
```

4. Tidy Data

Show code
```{r}
#| label: tidy
#| warning: false

# Helper Function
classify_region <- function(entity) {
    case_when(
        entity %in% c(
            "Brazil", "Chile", "Colombia", "Dominican Republic",
            "Guatemala", "Mexico", "Panama", "Paraguay", "Peru", "Uruguay"
        ) ~ "Latin America",
        entity %in% c("United States", "Canada") ~ "North America",
        entity %in% c(
            "Germany", "France", "United Kingdom", "Sweden", "Denmark",
            "Norway", "Finland", "Netherlands", "Belgium", "Austria",
            "Switzerland", "Italy", "Spain", "Portugal", "Ireland",
            "Luxembourg", "Greece", "Cyprus", "Malta"
        ) ~ "Western Europe",
        entity %in% c(
            "Poland", "Czechia", "Slovakia", "Hungary", "Slovenia",
            "Estonia", "Lithuania", "Croatia", "Bulgaria",
            "Romania", "Serbia"
        ) ~ "Eastern Europe",
        entity %in% c("Japan", "South Korea", "Singapore", "Taiwan", "Hong Kong") ~ "East Asia",
        entity %in% c("Australia", "New Zealand") ~ "Oceania",
        entity %in% c("Israel", "Turkey") ~ "Middle East",
        entity %in% c("South Africa") ~ "Africa",
        TRUE ~ "Other"
    )
}

# Base data with regional classification
base_data <- income_inequality_processed |>
    mutate(
        region = classify_region(entity),
        redistribution_effect = gini_mi_eq - gini_dhi_eq
    )

# PANEL 1: Ridge plot data
ridge_data <- base_data |>
    filter(!is.na(gini_dhi_eq), region != "Other") |>
    group_by(region) |>
    filter(n() >= 10) |>
    ungroup() |>
    mutate(region = fct_reorder(region, gini_dhi_eq, .fun = median, .desc = FALSE))

# PANEL 2: Redistribution data
redistribution_data <- base_data |>
    filter(!is.na(redistribution_effect), !is.na(gini_mi_eq), !is.na(gini_dhi_eq)) |>
    group_by(entity, region) |>
    summarise(
        avg_redistribution = mean(redistribution_effect, na.rm = TRUE),
        avg_pretax_gini = mean(gini_mi_eq, na.rm = TRUE),
        avg_posttax_gini = mean(gini_dhi_eq, na.rm = TRUE),
        observations = n(),
        .groups = "drop"
    ) |>
    # Get the 15 countries with minimal redistribution
    slice_min(avg_redistribution, n = 15) |> 
    mutate(
        is_latin = region == "Latin America",
        bar_color = ifelse(is_latin, "highlight", "main"),
        text_color = ifelse(is_latin, "highlight", "main"),
        country_label = paste0(entity, "\n(", round(avg_pretax_gini, 2), ")"),
        entity = fct_reorder(entity, avg_redistribution, .desc = FALSE)
    )
```

5. Visualization Parameters

Show code
```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
colors <- get_theme_colors(
    palette = c(
        "highlight" = "#2b1731",   
        "main" = "#9995be",        
        "dark_line" = "#2b1731",   
        "text_dark" = "#4D4D4D",   
        "text_medium" = "#7F7F7F",  
        "text_light" = "#B2B2B2",  
        "grid_line" = "#EAEAEA"  
    )
)

### |- titles and caption ----
title_text <- str_glue("The Geography of Inequality and the Redistribution Gap")

subtitle_text <- str_glue(
    "The distribution of post-tax inequality is sharply divided by geography, with the<br>",
    "highest levels concentrated in <span style='color:", colors$palette["highlight"], ";'>**Latin America**</span>."
)

caption_text <- create_social_caption(
    tt_year = 2025,
    tt_week = 31,
    source_text =  "Our World in Data"
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----
# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
    base_theme,
    theme(
        # Text styling
        plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.2), color  = colors$title, margin = margin(b = 10)),
        plot.subtitle = element_markdown(family = fonts$subtitle, lineheight = 1.2, color = colors$subtitle, size = rel(0.78), margin = margin(b = 20)),
        
        # Axis elements
        axis.line = element_blank(), 
        axis.ticks = element_blank(), 
        
        # Grid elements
        panel.grid.major.y = element_line(color = "gray90",linetype = "solid", linewidth = 0.3),
        panel.grid.minor.y = element_blank(), 
        panel.grid.major.x = element_blank(), 
        panel.grid.minor.x = element_blank(), 
        
        # Axis elements
        axis.text = element_text(color = colors$text, size = rel(0.7)),
        axis.title.x = element_text(color = colors$text, face = "bold", size = rel(0.8), margin = margin(t = 15)),
        axis.title.y = element_text(color = colors$text, face = "bold", size = rel(0.8), margin = margin(r = 10)),
        
        # Legend elements
        legend.position = "plot",
        legend.title = element_text(family = fonts$tsubtitle, color = colors$text, size = rel(0.8), face = "bold"),
        legend.text = element_text(family = fonts$tsubtitle, color = colors$text, size = rel(0.7)),
        
        # Plot margin
        plot.margin = margin(t = 15, r = 15, b = 15, l = 15),
    )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

Show code
```{r}
#| label: plot
#| warning: false

# PANEL 1: Ridge plot
ridge_plot <- ridge_data |>
    ggplot(aes(x = gini_dhi_eq, y = region)) +
    # Geoms
    geom_density_ridges(
        aes(fill = ifelse(region == "Latin America", "highlight", "main")),
        scale = 2.2,
        alpha = 0.75,
        bandwidth = 0.015,
        rel_min_height = 0.01,
        quantile_lines = TRUE,
        quantiles = 2,
        color = colors$palette["dark_line"]
    ) +
    # Scales
    scale_fill_manual(
        values = colors$palette,
        guide = "none"  
    ) +
    scale_x_continuous(
        name = "Post-tax Gini Coefficient",
        breaks = seq(0.15, 0.55, 0.05),
        labels = number_format(accuracy = 0.01),
        limits = c(0.15, 0.6),
        expand = expansion(mult = c(0.02, 0.02))
    ) +
    scale_y_discrete(name = NULL, expand = expansion(mult = c(0.05, 0.15))) +
    # Labs
    labs(
        title = "A. The Geography of Inequality",
        subtitle = paste0(
            "The distribution of post-tax inequality is sharply divided by geography,<br>",
            "with the highest levels concentrated in <span style='color:", colors$palette["highlight"], ";'>**Latin America**</span>."
        )
    ) 

# PANEL 2: Minimal Redistributors
redistributors_plot <- redistribution_data |>
    ggplot(aes(x = entity, y = avg_redistribution)) +
    # Geoms
    geom_col(aes(fill = bar_color), width = 0.7, alpha = 0.85) +
    geom_text(
        aes(label = round(avg_redistribution, 3), color = bar_color),
        hjust = -0.1,
        vjust = 0.5,
        size = 3.2,
        fontface = "bold"
    ) +
    # Scales
    scale_y_continuous(
        name = "Redistribution Effect\n(How Much Inequality Was Reduced)",
        breaks = seq(0, 0.16, 0.04),
        labels = number_format(accuracy = 0.01),
        limits = c(0, 0.2),
        expand = expansion(mult = c(0, 0.02))
    ) +
    scale_x_discrete(
        name = "Countries ordered by redistribution effectiveness\n(numbers show pre-tax inequality)",
        labels = redistribution_data$country_label,
        expand = expansion(mult = c(0.02, 0.02))
    ) +
    scale_fill_manual(
        values = colors$palette,
        guide = "none"  
    ) +
    scale_color_manual(
        values = colors$palette,
        guide = "none"  
    ) +
    coord_flip() +
    # Labs
    labs(
        title = "B. The Global Redistribution Gap",
        subtitle = paste0(
            "Countries with minimal redistribution despite high pre-tax inequality.<br>",
            "Countries like <span style='color:", colors$palette["highlight"], ";'>**Dominican Republic**</span> and ",
            "<span style='color:", colors$palette["highlight"], ";'>**Brazil**</span> highlight Latin America's pattern."
        )
    ) +
    # Theme
    theme(
        # Remove all gridlines
        panel.grid.major.y = element_blank(),
        
        # Remove x-axis text and ticks
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        
        # Adjust y-axis text size for readability with pre-tax labels
        axis.text.y = element_text(size = rel(0.95), lineheight = 0.9)
    )

### |-  combined plot ----
combined_plots <- ridge_plot | redistributors_plot

combined_plots <- combined_plots +
    plot_annotation(
        title = title_text,
        subtitle = subtitle_text,
        caption = caption_text,
        theme = theme(
            plot.title = element_text(
                size = rel(1.8),
                family = fonts$title,
                face = "bold",
                color = colors$title,
                lineheight = 1.1,
                hjust = 0.5,
                margin = margin(t = 5, b = 5)
            ),
            plot.subtitle = element_markdown(
                size = rel(1),
                family = fonts$subtitle,
                color = alpha(colors$subtitle, 0.9),
                lineheight = 1.2,
                hjust = 0.5,
                margin = margin(t = 5, b = 10)
            ),
            plot.caption = element_markdown(
                size = rel(0.7),
                family = fonts$caption,
                color = colors$caption,
                hjust = 0.5,
                margin = margin(t = 10)
            )
        )
    )
```

7. Save

Show code
```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plots, 
  type = "tidytuesday", 
  year = 2025, 
  week = 31, 
  width  = 12,
  height = 12
  )
```

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      patchwork_1.3.0 ggridges_0.5.6  glue_1.8.0     
 [5] scales_1.3.0    janitor_2.2.0   showtext_0.9-7  showtextdb_3.0 
 [9] sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0  
[13] stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2     readr_2.1.5    
[17] tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0
[21] pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       httr2_1.0.6        xfun_0.49          htmlwidgets_1.6.4 
 [5] gh_1.4.1           tzdb_0.5.0         yulab.utils_0.1.8  vctrs_0.6.5       
 [9] tools_4.4.0        generics_0.1.3     parallel_4.4.0     curl_6.0.0        
[13] gifski_1.32.0-1    fansi_1.0.6        pkgconfig_2.0.3    ggplotify_0.1.2   
[17] lifecycle_1.0.4    compiler_4.4.0     farver_2.1.2       munsell_0.5.1     
[21] codetools_0.2-20   snakecase_0.11.1   htmltools_0.5.8.1  yaml_2.3.10       
[25] crayon_1.5.3       pillar_1.9.0       camcorder_0.1.0    magick_2.8.5      
[29] commonmark_1.9.2   tidyselect_1.2.1   digest_0.6.37      stringi_1.8.4     
[33] rsvg_2.6.1         rprojroot_2.0.4    fastmap_1.2.0      grid_4.4.0        
[37] colorspace_2.1-1   cli_3.6.4          magrittr_2.0.3     utf8_1.2.4        
[41] withr_3.0.2        rappdirs_0.3.3     bit64_4.5.2        timechange_0.3.0  
[45] rmarkdown_2.29     tidytuesdayR_1.1.2 gitcreds_0.1.2     bit_4.5.0         
[49] hms_1.1.3          evaluate_1.0.1     knitr_1.49         markdown_1.13     
[53] gridGraphics_0.5-1 rlang_1.1.6        gridtext_0.1.5     Rcpp_1.0.13-1     
[57] xml2_1.3.6         renv_1.0.3         vroom_1.6.5        svglite_2.1.3     
[61] rstudioapi_0.17.1  jsonlite_1.8.9     R6_2.5.1           fs_1.6.5          
[65] systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in tt_2025_31.qmd.

For the full repository, click here.

10. References

  1. Data Sources:
  • TidyTuesday 2025 Week 31: [Income Inequality Before and After Taxes](https://github.com/rfordatascience/tidytuesday/blob/main/data/2025/2025-08-05)
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